ZDNet’s Steve Ranger has outlined three reasons why AI is currently essential, and how, expanding on these factors that together creates what he calls a “tipping point after which the use of artificial intelligence will become commonplace”.
Ranger refers to the Leading Edge Forum’s report that outlines a formula for machine intelligence innovation, and the three main ingredients are big data, software and hardware advances and cloud business models.
Rangers breaks down each section, starting with Big Data:
He explains how large unstructured data sets are very applicable for training machine intelligence, and the number of these around increases rapidly. “Initiatives such as language translation and image, facial, activity and emotion recognition – are based on predictive analytics that get more accurate as the data behind them gets richer”, Rangers writes.
With the rise of social media platforms, there has been a massive increase in data to exploit, with companies such as Facebook applying the face recognition feature first, and Google using machine translation as it has aggregated the best set of multilingual documents.
“Looking ahead, new and established MI companies will use millions of internet images, videos and podcasts of people smiling, laughing, frowning, talking, arguing, holding hands, walking, playing football and so on as the basis for unprecedented emotion and activity recognition capabilities. MI is now clearly among the most important Big Data applications”, the report reads.
Moving onto the next step – Software and Hardware advances, neural networks and parallel processing is introduced. These are essential development tools for AI as they are the closest resemblance to how the human brain actually works. Rangers highlights the emergence of GPU-based computing, and its ability to accelerate neural network processing capabilities.
“Taken together, deep learning software and parallel processing hardware now provide a powerful [machine intelligence] platform,” the report reads.
The last of the three is Cloud Business Models, deemed the single best reason that the field is so energised today, according to the report. “We are essentially seeing the merger of machine intelligence with cloud economics”.
Prior to the introduction of the cloud most AI work was isolated and very pricey, but the economics of the could now enables machine learning capabilities (facial recognition, translating languages etc), will be affordable and accessible for everyone.
“It is this realization that is triggering both the explosion of highly specialized MI start-ups, as well as the major machine intelligence pushes at Google, Facebook, Microsoft,Apple, IBM and their various global rivals, ” the report reads.
The researchers behind the report have created a 10-point-plan for organisations that wants to prepare for machine intelligence:
The researchers set out a 10 point plan for organisations that want to prepare for machine intelligence:
1. Embrace the idea that machine intelligence will matter to your organization.
2. Identify which forms could be most important to your firm.
3. Check out relevant start-ups and developments.
4. Understand which parts of your firm could be safely run by algorithms.
5. Determine which internal and external data sets have the most potential.
6. Assess the extent to which your firm’s key professional expertise can be automated.
7. Try out deep learning, neural computing and other technologies.
8. Map the relevant MI services and technologies to your firm’s value chain.
9. Develop machine intelligence experts in your organisation.
10. Factor AI advances into your strategic planning.
The report can be downloaded from this site: https://leadingedgeforum.com/publication/formula-for-machine-intelligence-innovation/
The article was first published at: http://www.zdnet.com/article/three-reasons-why-ai-is-taking-off-right-now-and-what-you-need-to-do-about-it/#ftag=RSSbaffb68?utm_medium=twitter